Efficient Trajectory Planning Research Articles

Spatio-Temporal Joint Optimization-Based Trajectory Planning Method for Autonomous Vehicles in Complex Urban Environments.

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio-temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio-temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments.

Accurate data-driven sliding mode parking control for autonomous ground vehicles with efficient trajectory planning in dynamic industrial scenarios

Accurate data-driven sliding mode parking control for autonomous ground vehicles with efficient trajectory planning in dynamic industrial scenarios

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

Efficient Trajectory Planning and Dynamic Resource Allocation for UAV-Enabled MEC System

An Efficient Trajectory Planning Approach for Autonomous Ground Vehicles Using Improved Artificial Potential Field

In this paper, the concept of symmetry is utilized in the promising trajectory planning design of autonomous ground vehicles—that is, the construction and the solution of improved artificial potential field-based trajectory planning approach are symmetrical. Despite existing artificial potential fields (APF) achievements on trajectory planning in autonomous ground vehicles (AGV), applying the traditional approach to dynamic traffic scenarios is inappropriate without considering vehicle dynamics environment and road regulations. This paper introduces a highly efficient approach for planning trajectories using improved artificial potential fields (IAPF) to handle dynamic road participants and address the issue of local minima in artificial potential fields. To begin with, potential fields are created with data obtained from other sensors. By incorporating rotational factors, the potential field will spin when the obstacle executes a maneuver; then, a safety distance model is also developed to limit the range of influence in order to minimize the computational burden. Furthermore, during the planning process, virtual forces using the gradient descent method are generated to direct the vehicle’s movement. During each timestep, the vehicle will assess whether it is likely to encounter a local minimum in the future. Once a local minimum is discovered, the method will create multiple temporary objectives to guide the vehicle toward the global minimum. Consequently, a trajectory that is both collision-free and feasible is planned. Traffic scenarios are carried out to validate the effectiveness of the proposed approach. The simulation results demonstrate that the improved artificial potential field approach is capable of generating a secure trajectory with both comfort and stability.

Obstacle Avoidance Trajectory Planning for Autonomous Vehicles on Urban Roads Based on Gaussian Pseudo-Spectral Method

Urban autonomous vehicles on city roads are subject to various constraints when changing lanes, and commonly used trajectory planning methods struggle to describe these conditions accurately and directly. Therefore, generating accurate and adaptable trajectories is crucial for safer and more efficient trajectory planning. This study proposes an optimal control model for local path planning that integrates dynamic vehicle constraints and boundary conditions into the optimization problem’s constraint set. Using the lane-changing scenario as a basis, this study establishes environmental and collision avoidance constraints during driving and develops a performance metric that optimizes both time and turning angle. The study employs the Gauss pseudo-spectral method to continuously discretize the state and control variables, converting the optimal control problem into a nonlinear programming problem. Using numerical solutions, variable control and state trajectories that satisfy multiple constraint conditions while optimizing the performance metric are generated. The study employs two weights in the experiment to evaluate the method’s performance, and the findings demonstrate that the proposed method guarantees safe obstacle avoidance, is stable, and is computationally efficient at various interpolation points compared to the Legendre pseudo-spectral method (LPM) and the Shooting method.

Online trajectory optimization for safe autonomous overtaking with active obstacle avoidance

Autonomous driving with active obstacle avoidance in dynamic urban environment has attracted significant attention to improve road safety and traffic efficiency. In this paper, an online optimization-based trajectory planning method for autonomous overtaking is developed to prevent collision and realize safe efficient driving. Unlike traditional methods which solve the overtaking problem with segmented reference path in multi-stages with integer variables, this paper proposes a novel dual-variable trajectory planning framework to get the optimal trajectory for active collision avoidance. First, the nonlinear non-differentiable collision-free constraint between vehicles is reformulated using dual problem optimization. Then, the optimal trajectory for longitudinal and lateral movement is obtained jointly in a receding horizon optimization framework based on the optimized dual variable considering the safety and dynamic performance. The key novelty lies in the reformulation of the nonlinear optimal trajectory planning problem and the formulated whole optimization framework can be solved using efficient open-source solvers for online computation. Simulation studies in different dynamic cases are provided to show the efficiency and robustness for safe driving. The method reaches safe overtaking performance with much less calculation time. Real-world experiment with a static obstacle demonstrates its capability for the safe efficient trajectory planning and online implementation in autonomous driving.

Trajectory tracking control of a pneumatically actuated continuum manipulator in the presence of obstacles by using terminal sliding mode control

This paper proposes an efficient trajectory planning and dynamic tracking controller scheme for a pneumatic continuum manipulator under the effect of material hysteresis. First of all, generalized governing nonlinear dynamic equations in the form of partial differential equations for pneumatic continuum manipulator dynamics are developed by using discrete Cosserat-rod theory, where the manipulator material hysteresis is modeled by using a fractional order Bouc–Wen model. Then, the trajectory planning for the end-effector of a continuum manipulator is proposed, which accounts for the static obstacles in the workspace and the Jacobian singularity. Subsequently, an adaptive terminal sliding mode controller for the joint space control combined with a simple PI controller for task space control is proposed. The proposed controller guarantees exponential convergence of the manipulator tip positional error in finite time, even in the existence of external disturbances and model uncertainties, without any need for prior knowledge of their upper bounds. Finally, the proposed controller is applied to a two-segment continuum manipulator, the trunk of Robotino-XT, through numerical simulations and the performance gain over two controllers proposed in the literature for similar pneumatic continuum manipulators is demonstrated.

Efficient Trajectory Planning and Tracking Control for Underactuated Crane

Efficient Trajectory Planning and Tracking Control for Underactuated Crane

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

PurposeThe paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.Design/methodology/approachThe proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.FindingsThe proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.Originality/valueThe novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

Multi-Stage RF Emitter Search and Geolocation With UAV: A Cognitive Learning-Based Method

This paper investigates the issue of searching and geolocating a radio frequency (RF) emitter with an unmanned aerial vehicle (UAV) in a rich scattering environment. The absence of knowledge of the characteristics of the target RF signals, the ground-to-air channels, and the potential geographical distribution of the RF emitter makes efficient trajectory planning (TP) and geolocating intractable. Since the average signal-to-noise ratio (SNR) of the signal captured by the UAV changes across the search area, state-of-the-art TP is inapplicable due to the assumption of a single problem domain, according to the No Free Lunch (NFL) theorem. In contrast, we develop a cognitive multi-stage search and geolocation (CMSG) framework, which adjusts the adopted TP algorithms in various search stages. In the stage with extremely-low SNRs, a random-walk-based TP is proposed to avoid meaningless detours. For stages with higher SNRs, the TP follows policies optimized by a novel search method based on direction of arrival (DOA) estimation and reinforcement learning. To alleviate the impairment of unreliable DOA estimations, empirical emitter position estimations are exploited to find the best flight directions. Simulation results confirm the advantages of the proposed CMSG over various baselines in lowering both failure rate and time consumption.

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework aiming to facilitate the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.

Detailed Analysis of Aircraft Fuel Flow Using Data from Flight Data Recorder

This paper aims to perform accurate prediction of fuel flow (FF) by employing various models: deep learning (DL), random forest (RF), generalized linear model (GLM), and the Eurocontrol Base of Aircraft Data (BADA) model, and to examine the link between FF and different aircraft performance parameters. The flight data set used in this study is obtained from real turbofan engine narrow-body aircraft performing short-distance domestic subsonic flights, containing a total of 2,674 cruise flights between 31 city pairs. Several statistical error analyses are conducted to compare the performance of the models. Root mean square error, mean absolute error, and mean squared error values for DL are calculated to be 0.01, 0.008, and 0.0001, respectively. On the coefficient of determination ( [Formula: see text]) test for validation, the DL model has the highest value of 0.94. Results of the analysis in this paper show that the DL model offers the best ability to predict FF in all statistical tests, which makes it the best-suited model to estimate FF. These findings can provide the means to make more efficient trajectory planning and forecasts in air traffic management, and more accurately predict fuel consumption of aircraft, thereby decreasing emission levels of carbon gases and of various pollutants and airline operating expenses from fuel costs.

A Unified Framework Integrating Decision Making and Trajectory Planning Based on Spatio-Temporal Voxels for Highway Autonomous Driving

Intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the ‘voxel expansion’ algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work.

Real-Time Trajectory Planning and Control for Constrained UAV Based on Differential Flatness

The trajectory planning of UAV with nonholonomic constraints is usually taken as differential algebraic equation to solve the optimal control problem of functional extremum under the condition of inequality constraints. However, it can be challenging to meet the requirements of real-time for the high complexity. A differential flat theory based on B-spline trajectory planning can replace the optimal control problem with nonlinear programming and be a good means to achieve the efficient trajectory planning of an UAV under multiple dynamic constraints. This research verifies the feasibility of this theory with actual flight experiments.

Total Suppression of High-Frequency Transient Oscillations in Dual-Active-Bridge Series-Resonant Converter by Trajectory-Switching Modulation

Dual-active-bridge series-resonant converter (DABSRC) is increasingly used in many emerging power electronics applications requiring fast dynamic responses. However, under conventional transient phase-shift modulation strategy, DABSRC generally suffers from large-amplitude transient oscillations when its phase-shift angle is changed abruptly by a high-gain controller. These oscillations occur at the beat frequency, which results from the interaction between the switching-frequency and resonant-frequency components in the series-resonant tank during transient states. Besides incurring high voltage and current stresses on the power-stage devices, these transient oscillations also span many switching cycles between the original and new steady states and cause perturbations to the output voltage of DABSRC, thereby degrading its dynamic performance and output voltage quality. To mitigate these problems, a new transient modulation strategy, known as trajectory-switching modulation (TSM), is proposed for achieving an accurate and computationally efficient trajectory planning of the resonant voltages and currents of DABSRC during transient states, and its basic operation is to govern the transient switching patterns of the gating signals according to a simple set of closed-form equations. The proposed TSM strategy can guarantee convergence to the next new steady state within about one switching cycle and avoid needing costly sensors and complex computation for implementation, and it is inherently compatible with high-gain controllers for realizing oscillation-free fast dynamic responses.

EDTP: Energy and Delay Optimized Trajectory Planning for UAV-IoT Environment

In modern days, UAV (Unmanned Aerial Vehicle) are being extensively used in various fields like military, healtcare, security, government sectors, supervision, home delivery agents, etc. They significantly enhance the potential of IoT devices by processing their data. However, issues like efficient trajectory planning, security and privacy protection, task scheduling, a delegation of tasks from one UAV to another in multi-UAV systems, etc., require rigorous research and analysis. In this paper, we consider a multi-UAV multi-IoT network, which is divided into certain hexagonal cells. Each cell consists of some IoT devices and a UAV to process the data those IoT devices collect from the environment. IoT devices in each cell are grouped into clusters so that UAVs hover only above cluster heads and midpoints of cell boundaries to collect and delegate tasks whenever required. Provision of both single and multi-hop task delegation exists in our system. The schedule is formed based on the UAV’s next intended arrival time as computed by cluster heads in the cell using the Auto-Regressive Moving Average (ARMA) model. Timestamps of visiting midpoints of cell boundaries are fitted between predicted next arrival times of visiting cluster heads. Energy consumption and time of transition from one hovering point to another can be optimized. However, the most energy-efficient solution may not necessarily be the most delay efficient. A multi-objective optimization technique is applied to identify the Pareto-optimal front and select the best possible solution. Simulation results show that compared to other trajectory planning algorithms viz. Dijkstra’s and HEA, our proposed technique saves much more energy (approx 78% over the other two) and time (42% over the other two).

Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest both academically and in automotive industry. Time efficiency and safety are of key importance and here a two-step procedure is proposed. First, a convex optimization problem is solved, formulated as a support vector machine (SVM), in order to represent the surrounding environment of the ego vehicle and classify the search space as obstacles or obstacle free. This gives a reduced complexity search space and an A* algorithm is used in a state space lattice in 4 dimensions including position, heading angle and velocity for simultaneous path and velocity planning. Further, a heuristic derived from the SVM formulation is used in the A* search and a pruning technique is introduced to significantly improve search efficiency. Solutions from the proposed planner is compared to optimal solutions computed using optimal control techniques. Three traffic scenarios, a roundabout scenario and two complex takeover maneuvers, with multiple moving obstacles, are used to illustrate the general applicability of the proposed method.

Geodesic Approach for an Efficient Trajectory Planning of Mobile Robot Manipulators

In this paper, the geodesic approach has been employed for an effective, optimal, accurate and smooth trajectory planning of a mobile robot manipulator mechanism. Generally, geodesic can be described as the shortest curvature between two loci on a Riemannian manifold. In order to attain the planned end-effector motion, Riemannian metrics has been consigned to the forward kinematics of mobile robot wheel as well as the mobile robot manipulator workspace. The rotational angles of wheel and joint kinematic parameters are chosen as local coordinates of spaces to represent Cartesian trajectories for mobile wheel rotation trajectories and joint trajectories respectively. The geodesic equalities for a given set of boundary conditions are evaluated for the chosen Riemannian metrics and the computational results of the geodesic equations have been shown. So as to verify and validate the efficiency of the chosen geodesic scheme, the method has been implemented for the motion planning and optimization of a mobile robot with a simple 3R manipulator installed upon its platform.

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

A machine learning based energy efficient trajectory planning approach for industrial robots

Towards an energy efficient trajectory planning of industrial robot (IR), this paper proposes a machine learning based approach. Within the context, the IR’s movements are digitalised in joint space first, which allows using data attributes to represent IR’s trajectories. Moreover, a set of designed trajectories which can address IRs workspace are followed by the IR, and meanwhile, the energy consumption is measured. Then data sets are generated by combining the trajectory data and measured energy consumption data, and they are used to train a machine learning model. On top of that, the trained model provides a fitness function to evolution based or swarm-intelligence based algorithms to obtain a near-optimal or optimal trajectory. Finally, a simplified case study is demonstrated to validate the proposed method. The method provides a direct connection between joint control and energy efficiency objective, by which the solution space can be obviously relaxed, compared to the existing methods.